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Revista de Nutrição

Print version ISSN 1415-5273On-line version ISSN 1678-9865

Rev. Nutr. vol.27 no.2 Campinas Mar./Apr. 2014

http://dx.doi.org/10.1590/1415-52732014000200004 

Original Articles

Association between sociodemographics factors and dietary patterns during pregnancy

Associação entre fatores sociodemográficos e padrões de consumo alimentar durante a gestação

Maria Beatriz Trindade De Castro 1  

Rita Adriana Gomes De Souza 2  

Ana Amélia Freitas Vilela 1  

Gilberto Kac 1  

1Universidade Federal do Rio de Janeiro, Instituto de Nutrição Josué de Castro, Observatório de Epidemiologia Nutricional. Av. Carlos Chagas Filho, 367, Bloco J, 2º andar, Cidade Universitária, Ilha do Fundão, 21941-902, Rio de Janeiro, RJ, Brasil. Correspondência para/Correspondence to: MBT CASTRO. E-mail: <mbtcastro@gmail.com>

2Universidade Federal de Mato Grosso, Instituto de Saúde Coletiva, Programa de Pós-Graduação em Saúde Coletiva. Cuiabá, MT, Brasil

ABSTRACT

OBJECTIVE:

To investigate the association between socio-demographic factors and dietary patterns in pregnancy.

METHODS:

Cross-sectional study with baseline data from a cohort of 421 postpartum women aged 18 and 45 years resident in Rio de Janeiro, Brazil. Dietary intake was evaluated with a validated food frequency questionnaire at 15 days following delivery, having as time frame the second and third pregnancy trimesters. Dietary patterns were identified using factor analysis for principal components analysis. The association between socio-demographic factors and the identified dietary patterns was assessed with multiple linear regression analysis.

RESULTS:

Two dietary patterns were identified: i) healthy: fruits; green vegetables; vegetables; fish; roots, corn and potato; milk and dairy and herbal tea mate, and negatively loadings for alcohol and coffee and ii) mixed: rice; bean; flour and pasta; breads; cake and cookies; soda and juice; sugar and sweets; fatty foods; meats; chicken; and eggs. The linear regression showed that the income (β=0.0002; 95%CI: 0.0002-0.0004) and schooling (β=0.0491; 95%CI: 0.0264-0.0718) were positively associated with healthy pattern, and parity (β=-0.1044; 95%CI: -0.1665- -0.0423) and skin color (β=-0.3102; 95%CI: -0.5256- -0.0947) were negatively associated. Skin color (β=0.1647; 95%CI: 0.0378- 0.2916) and marital status (β=0.1065; 95%CI: 0.0062- 0.2067) were positively associated with mixed pattern and income (β=-0.0001; 95%CI:-0.0002- -0.0001) and schooling (β=-0.0281; 95%CI: -0.0417- -0.0146) were negatively associated.

CONCLUSION:

Socio-demographic factors such as income, schooling, skin color, marital status and parity were associated with dietary patterns in this sample of postpartum women residents in Rio de Janeiro.

Key words: Demographic data; Factor analysis; Food consumption; Pregnant women; Socioeconomic factors

RESUMO

OBJETIVO:

Investigar a associação entre fatores sociodemográficos e padrões de consumo alimentar gestacional.

MÉTODOS:

Estudo transversal, com dados da linha de base de uma coorte com 421 puérperas entre 18 e 45 anos, residentes no Rio de Janeiro. O consumo alimentar foi avaliado com um questionário de frequência alimentar validado, aplicado aos 15 dias do pós-parto, e teve como referência o segundo e o terceiro trimestres gestacionais. Os padrões alimentares foram identificados a partir da análise fatorial por meio de análise de componentes principais. A associação entre os fatores sociodemográficos e os padrões alimentares foi avaliada com regressão linear múltipla.

RESULTADOS:

Foram identificados dois padrões alimentares: i) saudável: frutas; folhosos; hortaliças; peixe; raízes, milho e batata; leite e derivados e mate, e, negativamente para o consumo de álcool e café; e ii) misto: arroz; feijão; farinhas e macarrão; pães; bolo e biscoito; refrigerante e suco; açúcares e doces; alimentos gordurosos; carnes; frango e ovos. Na regressão linear, a renda (β=0,0002, IC95%: 0,0002-0,0004) e a escolaridade (β=0,0491, IC95%: 0,0264-0,0718) associaram-se positivamente ao padrão saudável, e a paridade (β=-0,1044, IC95%: -0,1665- -0,0423) e a cor da pele (β=-0,3102, IC95%: -0,5256- -0,0947) associaram-se negativamente. A cor da pele (β=0,1647, IC95%: 0,0378-0,2916) e o estado marital (β=0,1065, IC95%: 0,0062-0,2067) associaram positivamente ao padrão misto; e a renda (β=-0,0001, IC95%: -0,0002- -0,0001) e a escolaridade (β=-0,0281, IC95%: -0,0417- -0,0146) associaram-se negativamente.

CONCLUSÃO:

Fatores sociodemográficos como a renda, a escolaridade, a cor da pele, o estado marital e a paridade influenciaram os padrões alimentares nessa amostra de puérperas residentes no Rio de Janeiro.

Palavras-Chave: Dados demográficos; Análise fatorial; Consumo de alimentos; Gestantes; Fatores socioeconômicos

INTRODUCTION

Dietary nutrient adequacy during pregnancy is usually assessed by estimating the intake of specific macro and micronutrients, foods, and food groups1 - 4. Although these studies are important, diet consists of a combination of meals, nutrients, and foods5, and is determined by social, cultural6 , 7, physiologic, genetic8, and demographic9 factors.

Studies on nutritional epidemiology have analyzed the diet of populations using their dietary patterns6 , 10 and associating these patterns with sociodemographic determinants during pregnancy9 , 11 - 13. Principal Component Analysis (PCA) is one of the methods used for assessing dietary patterns14. This analytical procedure allows assessing a diet within a multidimensional context of exposure6 , 10 by reducing the number of dietary variables, which are grouped into factors composed of correlated foods. Analysis of dietary patterns assesses the global effect of diet on health and identifies population groups at the greatest risk of diet-related morbidities15 , 16.

Northstone et al.9 used a Food Frequency Questionnaire (FFQ) to assess the food intake of 12,053 pregnant women on the third trimester of pregnancy and found five distinct dietary patterns. These authors found a positive association between schooling and the pattern called "health conscious". Arkkola et al.12found that the mother's age and schooling were positively associated with "healthy" and "low-fat foods" patterns when they studied 3,730 Finnish women. To date, only one study about the dietary patterns of pregnant women was published in Brazil17.

Food intake during pregnancy varies greatly18. The analysis of dietary patterns can be a guide for specific groups of women according to their demographic and socioeconomic determinants, promoting a healthier diet during pregnancy. Hence, the aim of the present study was to investigate if demographic and socioeconomic factors are associated with dietary patterns during pregnancy.

METHODS

Study design and population

This cross-sectional study used the baseline data of a prospective cohort of 479 women aged 18 to 45 years. The study was conducted from May 1999 to April 2001. The prospective cohort design included 15 months of recruitment and 9 months of follow-up, and collected data on the following occasions: 15 days after delivery (baseline), and 2, 6, and 9 months postpartum. The participants were recruited for the cohort during prenatal and newborn Bacillus Calmette-Guérin (BCG) immunization visits, performed at the Municipal Health Center Marcolino Candau, and immediately after delivery at the maternity hospital of the Praça XV, both located in the municipality of Rio de Janeiro, Brazil.

The present study analyzed only the baseline data. Forty-seven women aged less than 18 years and two with a daily total energy intake in excess of 6,000kcal were excluded. Of the 430 (100.0%) eligible women, 421 (97.9%) answered the FFQ.

Dependent variable

A total of 421 postpartum women answered the FFQ approximately 15 days after delivery. The FFQ, previously validated by Sichieri & Everhart19, was administered to collect food intake data during the second and third trimesters of pregnancy1. This instrument was validated to investigate food intake during the six months prior to its administration19.

The dietary patterns were obtained by grouping the 81 food items and beverages listed in the FFQ into 19 food groups: 1) rice; 2) bean; 3) breads; 4) flours and pasta; 5) roots, corn and potato (cassava, yam, corn, and potatoes); 6) meats (steak, oxtail, pork, and giblets); 7) chicken; 8) eggs; 9) fish; 10) green vegetables (lettuce, kale, cabbage, and chicory); 11) vegetables (okra, chayote, cucumber, onion, squash, zucchini, carrots, pea pods, beets, cauliflower, red/green/yellow peppers, and tomatoes); 12) fruits (orange, banana, papaya, apple, melon, avocado, pineapple, mango, grapes, guava, and pear); 13) milk and dairy products (whole milk, skim milk, yogurt, cheese, and cream cheese); 14) sugar and sweets; 15) cakes and cookies-crackers (cookies and savory biscuits); 16) soda and juices; 17) fatty foods (butter/margarine, bacon, mayonnaise, pizza, savory snacks, french fries, popcorn, patty, and sausage; 18) alcoholic beverages and coffee (wine, beer, and other alcoholic beverages); and 19) herbal mate tea. Some foods like rice, bean, breads, and herbal mate tea were not grouped because they were consumed frequently. Other foods such as chicken, eggs, and fish were placed in individual groups because they have a unique nutritional composition. Alcoholic beverages and coffee were kept in the same group because they were consumed only occasionally during pregnancy, and also because they should be avoided during this time.

The factor analysis for PCA determined the dietary patterns of the women during pregnancy. Commonalities greater than 0.30 determined whether the groups were correctly correlated. The following criteria established the number of factors (patterns): (1) eigenvalues >1.50 and (2) the Scree test plots. After varimax orthogonal rotation, factor loadings above 0.20 limited the intercorrelation between the dietary variables given by the factors10 , 20. The internal consistency of dietary pattern identified was given by Cronbach's alpha. Next, the factors were labeled according to the most prevalent foods14 - 16.

Independent variables

The following sociodemographic and obstetric variables were selected as independent: family income (reais), schooling (years), skin color (white or brown/black), marital status (married/living together or single/other) and parity (number of deliveries). Pregnancy-related information was collected at baseline using a structured questionnaire.

Covariates

Information about total energy intake (calories), pre-pregnancy Body Mass Index (BMI) [pre-pregnancy weight (kg)/height2 (meters), and age (years) were used as adjustment variables in data analysis.

Total energy intake was determined by a program developed by Sichieri21 in the statistical package Statistical Analysis System (SAS) version 8.2. The usual food intake was converted into daily total energy intake by multiplying each food consumed in standardized portions22 by the daily intake frequency: more than 3 times a day; 2 to 3 times a day; once a day; 5 to 6 times a week; 2 to 4 times a week; once a week; 1 to 3 times a month; never or hardly ever. The energy content of the foods was given by the nutrient composition database from Escola Paulista de Medicina 23. If a food was not listed in the aforementioned database, the food composition table of the National Household Expenditure Study was used24.

The pre-pregnancy BMI was obtained by pre-pregnancy weight reported by women and their height was measured by a stadiometer of the brand Holtain-Harpeden (Crymych, United Kingdom) with an accuracy of 0.1 cm. The women were measured barefoot.

Statistical analysis

The Student's t test and the Chi-square test compared the independent variables and covariates according to the forth quartile of identified dietary patterns. Bivariate linear regression analysis considered the dietary pattern scores as the outcome variable. Age and total energy intake were used as adjustment variables. Although the pre-pregnancy BMI of women with healthy and mixed dietary patterns were not different distributed, this covariate was used as an adjustment variable because its p-value was <0.20 in the bivariate linear regression analysis. The dependent variable of the final multiple linear regression model was the score of each dietary pattern, and the model was adjusted for age, total energy intake, and pre-pregnancy BMI. The significance level was set at p<0.05.

Ethical aspects

The study was approved by the Research Ethics Committee of the Núcleo de Estudos em Saúde Coletiva (NESC) of the Universidade Federal do Rio de Janeiro (UFRJ) under Protocol number 041/98. All participants signed an Informed Consent Form before they joined the study. More information about the study design and population can be found elsewhere4 , 25.

RESULTS

Two dietary patterns were identified and labeled healthy and mixed. Their eigenvalues were 2.24% and 2.22%, respectively. The accumulated variance was 23.48%. The healthy pattern explained 11.79% of the total variance and consisted of fruits; green vegetables; vegetables; fish; roots, corn and potato; milk and dairy products; and herbal mate tea; and inversely with alcohol and coffee intake. The mixed pattern explained 11.69% of the total variance and consisted of rice; bean; flours and pasta; breads; cake and cookies-crackers; soda and juices; sugar and sweets; fatty foods; meats; chicken, and eggs (Table 1).

Table 1 Distribution of factor loadings and communalities (h2) of the two dietary patterns identified in the gestational period(1) in a cohort with 421 postpartum women. Rio de Janeiro, Brazil, 1999-2001. 

Foods and food groups Dietary pattern
Factor loadings2 h2
Healthy Mixed
Fruits 0.537 0.175 0.71
Green vegetables 0.455 -0.073 0.72
Vegetables 0.439 0.039 0.73
Fish 0.261 0.034 0.60
Roots, corn, and potato 0.296 0.083 0.68
Milk and dairy products 0.443 -0.049 0.74
Alcoholic beverages and coffee -0.210 0.096 0.59
Herbal mate tea 0.283 -0.070 0.64
Rice -0.306 0.355 0.59
Bean -0.201 0.332 0.55
Flours and pasta 0.029 0.260 0.59
Breads -0.056 0.555 0.69
Cakes and cookies-crackers 0.101 0.266 0.60
Soda and juices 0.130 0.307 0.74
Sugar and sweets -0.034 0.257 0.54
Fatty foods 0.075 0.516 0.66
Meats 0.291 0.328 0.66
Chicken -0.029 0.250 0.70
Eggs -0.095 0.271 0.62
Number of items 8 11
Eigenvalues 2.24 2.22
Final communalities 1.43 1.41
% of variance explained 11.79 11.69
% of cumulative variance explained 11.79 23.48

Note: 1 Food intake refers to second and third gestational trimester; 2Factor analysis for principal component analysis.

Women who adhered more to the healthy pattern were older (28.4x24.7 years, p<0.001), had higher income (R$1.038,00xR$569,00 reais, p<0.001), and higher schooling (8.1x6 years, p<0.001) than those who adhered more the mixed pattern. White and married women or those living with a partner also preferred the healthy pattern as opposed to the brown/black women who were single/other (Table 2).

Table 2 Mean values and proportions of selected variables from the dietary patterns identified in the gestational period(1) in a cohort with 421 postpartum women. Rio de Janeiro, Brazil, 1999-2001. 

Variables Dietary pattern p value*
Healthy Mixed
Mean SD Mean SD
Age (years) 28.4 5.79 24.7 5.76 <0.001
Total energy intake (kcal) 2.867 653 3.874 666 <0.001
Pre-pregnancy BMI (kg/m²) 22.6 3.24 22.5 4.05 0.889
Income (reais) 1.038 995 569 414 <0.001
Schooling (years) 8.1 3.45 6.0 2.75 <0.001
N % N % p value*
Parity 0.746
<1 22 38.6 29 41.4
=2 35 61.4 41 58.6
Skin color <0.001
White 38 54.3 26 24.5
Brown/Black 32 45.7 80 75.5
Marital status 0.029
Married or stable partnership 59 84.3 74 69.8
Single/Other 11 15.7 32 30.2

Note: *The p value refers to the Student's t test or Chi-square test. 1Food intake refers to second and third gestational trimester. SD: Standard Deviation; BMI: Body Mass Index.

The bivariate linear regression model showed that income and schooling were positively associated with the healthy pattern. Brown or black skin color was positively associated with the mixed pattern and negatively associated with schooling. The multiple linear regression model found that income and schooling were positively associated with the healthy pattern and negatively associated with parity and skin color. Income and schooling were negatively associated with the mixed pattern, and marital status and skin color were positively associated with it. The multiple regression model was adjusted for age, total energy intake, and pre-pregnancy BMI (Table 3).

Table 3 Bivariate linear regression and multiple linear regression of selected variables from the dietary patterns identified in the gestational period in a cohort with 421 postpartum women. Rio de Janeiro, Brazil, 1999-2001. 

Healthy pattern
Variables Bivariate linear regression Multiple linear regression
ß p value* 95%CI ß p value** 95%CI
Age (years) 0.0184 0.005 [0.0056] - [0.0312] - - -
Total energy intake (kcal) 0.0003 <0.001 [0.0002] - [0.0004] - - -
Pre-pregnancy BMI (kg/m²) -0.0068 0.514 [-0.0274] - [0.0137] - - -
Income (reais) 0.0003 <0.001 [0.0002] - [0.0004] 0.0002 <0.001 [0.0001] - [0.0004]
Schooling (years) 0.0493 <0.001 [0.0256] - [0.0729] 0.0491 <0.001 [0.0264] - [0.0718]
Parity (n) -0.0321 0.271 [-0.0893] - [-0.0252] -0.1044 0.001 [-0.1665] - [-0.0423]
Skin color†† -0.2062 0.063 [-0.4238] - [0.0114] -0.3102 0.005 [-0.5256] - [-0.0947]
Marital status††† -0.1464 0.108 [-0.3249] - [-0.0321] -0.1384 0.112 [-0.3094] - [0.0326]
Mixed pattern
Variables Bivariate regression Multiple regression
ß p value* 95%CI ß p value** 95%CI
Age (years) -0.0128 0.050 [-0.0256] - [-0.00001] - - -
Total energy intake (kcal) 0.0008 <0.001 [0.0007] - [0.0008] - - -
Pre-pregnancy BMI (kg/m²) -0.0160 0.124 [-0.0363] - [-0.0044] - - -
Income (reais) -0.0001 0.1363 [-0.0002] - [-0.00003] -0.0001 <0.001 [-0.0002] - [-0.0001]
Schooling (years) -0.0336 0.006 [-0.0572] - [-0.0099] -0.0281 <0.001 [-0.0417] - [-0.0146]
Parity (n) 0.0230 0.415 [-0.0324] - [0.0784] 0.0328 0.083 [-0.0043] - [0.0698]
Skin color†† 0.4242 <0.001 [0.2091] - [0.6393] 0.1647 0.011 [0.0378] - [0.2916]
Marital status††† 0.1213 0.182 [0.0570] - [0.2997] 0.1065 0.038 [0.0062] - [0.2067]

Note: *p value refers to bivariate linear regression; **p value refers to multiple linear regression; The final multiple linear regression model was adjusted for age, total energy intake, and pre-pregnancy BMI. Pre-pregnancy Body Mass Index (BMI): [Pre-pregnancy weight (kg)/height 2 (meters)] was obtained by pre-pregnancy weight reported by women and their height was measured; ††White or Brown/Black; †††Married or stable partnership or single/other.

DISCUSSION

Two dietary patterns were identified, a healthy pattern that explained most of the dietary variance and was characterized by fruits; green vegetables; vegetables; roots, corn and potato; milk and dairy products; fish; and herbal mate tea; and inversely with alcoholic beverages and coffee; and a mixed pattern characterized by rice; bean; breads; cakes and cookies-crackers; sodas and juices; fatty foods; meats, chicken, and eggs. Older women with higher schooling, higher income, lower parity, married or living with a partner, and white were more likely to adhere to the healthy pattern. The results of the multiple regression analysis model showed that higher income and schooling were positively associated with the healthy pattern, and higher parity and brown or black skin color were negatively associated with it. Married women or those living with their partners and those with brown or black skin color were positively associated with the mixed pattern, while income and schooling were negatively associated with it. Women who adhered more to the mixed pattern were more likely to be younger and have higher parity.

Among the study limitations, it is important to emphasize that even though the broader study comprises a prospective cohort of postpartum women, the analyses were based exclusively on baseline data, therefore the study has a cross-sectional design. Other limitations include the potential memory bias associated with the FFQ; the arbitrary decisions taken by the researchers, such as food grouping; number of extracted factors; and naming of the retained factors20. However, these decisions are inherent to PCA, which is being widely used in epidemiological studies on the dietary patterns of pregnant women9 , 11 , 12 , 26.

Women with higher schooling, higher income, lower parity, and white were more likely to adhere to the healthy dietary pattern, a finding corroborated by other studies. Northstone et al. 9studied a cohort of pregnant women in the Southeast region of England and found a pattern that they called "health conscious", characterized by the intake of salads, fruits, rice, pasta, oat, breakfast cereals, fish, fruit juices, and whole bread. Low schooling and multiple parities were negatively associated with this pattern. Arkkola et al. 12 studied the dietary patterns of a cohort of pregnant Finnish women and found that higher schooling was associated with the healthy pattern. In a population-based, cross-sectional study of 1,026 women aged 20 to 60 years living in the Brazilian South region, Alves et al. 27found that those with higher schooling and socioeconomic status adhered more frequently to the healthy pattern. The first Brazilian study to assess the dietary patterns of pregnant women included women attended in health care facilities located in the Brazilian South; this study found that high family income and schooling were positively associated with a healthy dietary pattern17.

In general, higher socioeconomic status and age tend to be associated with healthier food choices13 , 28. These results are expected since these factors are related to access to health services and consequently, to health-related information, and additionally, higher income grants better access to healthier foods29 since these are usually more expensive30. The study women had low income and schooling. Yet, it was possible to detect a positive association between income and schooling and healthier food choices. This is probably because women tend to prefer healthier foods when they are pregnant to promote better fetal development, even when these foods are nutrient poor31.

The mixed pattern consisted of foods with high energy and lipid contents, but also included items like rice and beans, considered commom in the Brazilian diet32 , 33. The women who adhered to this pattern were married or lived with a partner, had brown or black skin, and had lower income and schooling. Thompson et al. 26 found that the women who adhered to the "junk and fusion pattern", consisting of foods similar to those of the mixed pattern of the present study, were from middle and low socioeconomic levels. Olinto et al.32 studied 4,202 adults from a birth cohort from Pelotas (RS), a city in the Brazilian South region, and found that low schooling and income were associated with the common Brazilian pattern, which consisted of rice, beans, sugar, bread, coffee, butter/margarine.

In the present study, pregnant women with lower schooling and income and brown or black skin were more likely to prefer high-energy, refined carbohydrates, and high-fat foods, characteristic of the mixed pattern. Black women of low socioeconomic level usually do not begin prenatal care during the first trimester of pregnancy34, that may compromise the information they receive about a healthy pregnancy and consequently, proper diet during pregnancy. The changes in Brazilian eating habits seen in the last years included the replacement of healthier foods by high-fat, high-carbohydrate foods, especially those with sugar, although many people still consume the traditional diet35 , 36. Most of these foods are inexpensive and available everywhere, good reasons for women of low income and schooling to adhere more of these foods during pregnancy.

In summary, this study has shown that sociodemographic factors such as income, schooling, skin color, marital status, and parity were associated with the dietary patterns of pregnant women. These sociodemographic factors influenced the dietary patterns of a sample of pregnant women from Rio de Janeiro (RJ), emphasizing that women with higher schooling and income preferred the healthy pattern.

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Received: April 30, 2013; Revised: January 07, 2014; Accepted: January 06, 2014

CONTRIBUTORS MBT CASTRO contributed to the data analysis, interpretation of data, and drafting of the manuscript; RAG SOUZA and AAF VILELA contributed in the interpretation of data, and drafting of the manuscript; and G KAC participated in the design and coordination of the study, interpretation of data, and drafting the manuscript. All authors read, revised, and approved the final manuscript.

Support: Fundação Universitária José Bonifácio, Universidade Federal do Rio de Janeiro (Process: 8149/3) and the Fundação Carlos Chagas Filho for Amparo à Pesquisa do Estado do Rio de Janeiro (Protocol nº 26/171.275/98). Research, support of Rio de Janeiro State Conselho Nacional de Desenvolvimento Científico e Tecnológico.

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